Social mining for terroristic behavior detection through Arabic tweets characterization

Abstract

In the latest years, the use of social media has increased dramatically. Content, as well as media, are shared in Big Data volumes and this poses a critical requirement for the behavior supervision and fraud protection. The detection of terrorist behavior in the social media is essential to every country, but has complexities in both the supervision of shared content and in the understanding of behavior. Therefore, in this project an artificial intelligence enabled Detection Terrorist behavior system (ALT-TERROS) as a key priority was developed. The key requirements for a terrorist behavior detection system operating in the Kingdom are: (i) Data integration, (ii) Advanced smart analysis capacity and (iii) Decision making capability. The unique value proposition is based on a sophisticated integrated approach to the management of distributed data available on social media, which uses advanced social mining methods for the detection of patterns of terrorist behavior, its visualization and use for decision making. In addition, several critical issues related to the availability of APIs to handle Arabic text as well as the need to provide an end-to-end workflow from the extraction of textual and visual data over social media to the deliverable of advanced analytics and visualizations for rating mechanisms were highlighted. The key contribution of our approach is a testbed for the application of novel scientific approaches and algorithms for the rating of harm associated to social media content. The complexity of the problem does not allow hyper-optimistic solutions, but the combination of heuristic rules and advanced decision-making capabilities is toward the right direction. We contribute to the body of the theory of Sentiment Analysis for Arabic content and we also summarize a heuristic algorithm developed for the future. In the future research directions, we emphasize on the need to develop trusted Arabic thesaurus and corpus for the use sentiment analysis.